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I wish pip install automatically updated the requirements file like in some other languages. I look up some boilerplate in stack overflow every week for matplo

I wish pip install automatically updated the requirements file like in some other languages. I look up some boilerplate in stack overflow every week for matplotlib figures to look better. CSVs generated in excel cause errors when reading in python without a special incantation. It’s not super easy to set up multi machine training. AWS GPUs are extremely expensive. sklearn models, once saved, are not easy to rework into a different format to be used with other languages. I wish python could do compile-time checking instead of running for 30 minutes to then report a type mismatch. mypy is still not very good and in beta

Jugurtha 6y ago
OK. Asking because we've started building https://iko.ai https://iko.ai as an internal machine learning platform to solve actual problems we have faced delivering machine learning projects for our clients these past few years. So far, it has the following: - No-setup Jupyter environments with the most popular libraries pre-installed - Real-time collaboration on notebooks: see cursors and changes, etc. - Multiple versions of your notebooks - Long-running notebook scheduling with output that survives closed tabs and network disruptions. You can watch the notebook running live even on your phone without opening the JupyterLab interface. - Automatic experiment tracking: automatically detects your models, parameters, and metrics and saves them without you remembering to do so or polluting your notebook with tracking code - Easily deploy your model and get a "REST endpoint" so data scientists don't tap on anyone's shoulder to deploy their model, and developers don't need to worry about ML dependencies to use the models - Build Docker images for your model and push it to a registry to user it wherever you want - Monitor your models' performance on a live dashboard - Publish notebooks as AppBooks: automatically parametrize a notebook to enable clients to interact with it without exporting PDFs or having to build an application or mutate the notebook. This is very useful when you want to expose some parameters that are very domain specific to a domain expert. Much more on our roadmap. We're only focusing on actual problems we have faced serving our clients, and problems we are facing now. We always are on the lookout for problems people who work in the field have. What do you think?

Jugurtha 6y ago

OK. Asking because we've started building https://iko.ai https://iko.ai as an internal machine learning platform to solve actual problems we have faced delivering machine learning projects for our clients these past few years. So far, it has the following: - No-setup Jupyter environments with the most popular libraries pre-installed - Real-time collaboration on notebooks: see cursors and changes, etc. - Multiple versions of your notebooks - Long-running notebook scheduling with output that survives closed tabs and network disruptions. You can watch the notebook running live even on your phone without opening the JupyterLab interface. - Automatic experiment tracking: automatically detects your models, parameters, and metrics and saves them without you remembering to do so or polluting your notebook with tracking code - Easily deploy your model and get a "REST endpoint" so data scientists don't tap on anyone's shoulder to deploy their model, and developers don't need to worry about ML dependencies to use the models - Build Docker images for your model and push it to a registry to user it wherever you want - Monitor your models' performance on a live dashboard - Publish notebooks as AppBooks: automatically parametrize a notebook to enable clients to interact with it without exporting PDFs or having to build an application or mutate the notebook. This is very useful when you want to expose some parameters that are very domain specific to a domain expert. Much more on our roadmap. We're only focusing on actual problems we have faced serving our clients, and problems we are facing now. We always are on the lookout for problems people who work in the field have. What do you think?